# ---------------------------------------------------------------------------- #
#                              Data pre-processing                             #
# ---------------------------------------------------------------------------- #

context("Data loading and pre-processing")

sc <- DISCBIO(valuesG1msTest) # Reduced dataset used for testing

test_that("Loading datasets generate the expected output", {
  expect_equal(dim(valuesG1msTest), c(800, 15))
})

test_that("Data signature changes", {
  expect_equal(class(sc)[1], "DISCBIO")
  expect_equal(attr(class(sc), "package"), "DIscBIO")
})

# This function will be used only if the dataset has ERCC
sc <- NoiseFiltering(sc, plot = FALSE, export = FALSE, quiet = TRUE)

test_that("Noise filtering is added", {
  expect_equal(length(sc@noiseF), 163)
})

# In this case this function is used to normalize the reads
sc <- Normalizedata(
  sc,
  mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,
  downsample = FALSE, dsn = 1, rseed = 17000
)

test_that("Data is normalized", {
  expect_equal(class(sc@fdata), "data.frame")
  expect_output(str(sc@fdata), "708 obs. of  15 variables")
})

# This function can be used for:
# 1 - filtering and normalizing the dataset that has no ERCC.
# 2 - to normalize and filter genes and cells after the noise filtering.
sc <- FinalPreprocessing(
  sc,
  GeneFlitering = "NoiseF", export = FALSE, quiet = TRUE
)

test_that("Data is normalized", {
  expect_equal(dim(sc@fdata), c(163, 15))
})

# ---------------------------------------------------------------------------- #
#                              K-means clustering                              #
# ---------------------------------------------------------------------------- #

context("K-means clustering")

sc <- Clustexp(
  sc,
  clustnr = 2, cln = 2, bootnr = 10, quiet = TRUE, rseed = 17000
)
sc <- comptSNE(sc, rseed = 15555, quiet = TRUE, max_iter = 1, epoch = 10)

test_that("tSNE is computed", {
  expect_equal(class(sc@tsne), "data.frame")
  expect_output(str(sc@tsne), "15 obs. of  2 variables")
})

test_that("Cluster plots output is as expexted", {
  expect_equivalent(
    object = Jaccard(sc, Clustering = "K-means", K = 2, plot = FALSE, R = 5),
    expected = c(.683, .624),
    tolerance = .01
  )
  expect_equal(
    object = clustheatmap(sc, hmethod = "single", plot = FALSE),
    expected = c(1, 2)
  )
})

# --------------------------------- Outliers --------------------------------- #

context("Outliers")

Outliers <- FindOutliers(
  sc,
  K = 2, outminc = 5, outlg = 2, probthr = .5 * 1e-3, thr = 2^(-1:-40),
  outdistquant = .75, plot = FALSE, quiet = TRUE
)
Order <- pseudoTimeOrdering(sc, quiet = TRUE, export = FALSE)

test_that("Outliers are the expected", {
  expect_equivalent(Outliers, c(3, 10, 13))
  expect_equivalent(
    object = Order@kordering,
    expected = c(
      5, 6, 13, 2, 3, 1, 4, 7, 12, 9, 15, 8, 10, 11, 14
    )
  )
})